EXAM DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER REVIEWS | DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER VALID TEST GUIDE

Exam Databricks-Certified-Professional-Data-Engineer Reviews | Databricks-Certified-Professional-Data-Engineer Valid Test Guide

Exam Databricks-Certified-Professional-Data-Engineer Reviews | Databricks-Certified-Professional-Data-Engineer Valid Test Guide

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q75-Q80):

NEW QUESTION # 75
The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. Therecent_sensor_recordingstable contains an identifyingsensor_idalongside thetimestampandtemperaturefor the most recent 5 minutes of recordings.
The below query is used to create the alert:

The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger whenmean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?

  • A. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
  • B. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
  • C. The source query failed to update properly for three consecutive minutes and then restarted
  • D. Therecent_sensor_recordingstable was unresponsive for three consecutive runs of the query
  • E. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query

Answer: A

Explanation:
Explanation
This is the correct answer because the query is using a GROUP BY clause on the sensor_id column, which means it will calculate the mean temperature for each sensor separately. The alert will trigger when the mean temperature for any sensor is greater than 120, which means at least one sensor had an average temperature above 120 for three consecutive minutes. The alert will stop when the mean temperature for all sensors drops below 120. Verified References: [Databricks Certified Data Engineer Professional], under "SQL Analytics" section; Databricks Documentation, under "Alerts" section.


NEW QUESTION # 76
When investigating a data issue you realized that a process accidentally updated the table, you want to query the same table with yesterday's version of the data so you can review what the prior version looks like, what is the best way to query historical data so you can do your analysis?

  • A. TIME_TRAVEL FROM table_name WHERE time_stamp = date_sub(current_date(), 1)
  • B. DISCRIBE HISTORY table_name AS OF date_sub(current_date(), 1)
  • C. SELECT * FROM TIME_TRAVEL(table_name) WHERE time_stamp = 'timestamp'
  • D. SHOW HISTORY table_name AS OF date_sub(current_date(), 1)
  • E. SELECT * FROM table_name TIMESTAMP AS OF date_sub(current_date(), 1)

Answer: E

Explanation:
Explanation
The answer is SELECT * FROM table_name TIMESTAMP as of date_sub(current_date(), 1) FYI, Time travel supports two ways one is using timestamp and the second way is using version number, Timestamp:
1.SELECT count(*) FROM my_table TIMESTAMP AS OF "2019-01-01"
2.SELECT count(*) FROM my_table TIMESTAMP AS OF date_sub(current_date(), 1)
3.SELECT count(*) FROM my_table TIMESTAMP AS OF "2019-01-01 01:30:00.000" Version Number:
1.SELECT count(*) FROM my_table VERSION AS OF 5238
2.SELECT count(*) FROM my_table@v5238
3.SELECT count(*) FROM delta.`/path/to/my/table@v5238`
https://databricks.com/blog/2019/02/04/introducing-delta-time-travel-for-large-scale-data-lakes.html


NEW QUESTION # 77
A table in the Lakehouse namedcustomer_churn_paramsis used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records?

  • A. Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions.
  • B. Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.
  • C. Modify the overwrite logic to include a field populated by calling
    spark.sql.functions.current_timestamp() as data are being written; use this field to identify records written on a particular date.
  • D. Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.
  • E. Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.

Answer: B

Explanation:
Explanation
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with an existing cluster id and a notebook task, but also specifies a new cluster spec with some configurations. According to the documentation, if both an existing cluster id and a new cluster spec are provided, then a new cluster will be created for each run of the job with those configurations, and then terminated after completion. Therefore, the logic defined in the referenced notebook will be executed three times on new clusters with those configurations. Verified References: [Databricks Certified Data Engineer Professional], under "Monitoring & Logging" section; Databricks Documentation, under
"JobsClusterSpecNewCluster" section.


NEW QUESTION # 78
A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

  • A. Only the email and ltv columns will be returned; the email column will contain the string "REDACTED" in each row.
  • B. The email, age. and ltv columns will be returned with the values in user ltv.
  • C. The email and ltv columns will be returned with the values in user itv.
  • D. Only the email and itv columns will be returned; the email column will contain all null values.
  • E. Three columns will be returned, but one column will be named "redacted" and contain only null values.

Answer: A

Explanation:
The code creates a view called email_ltv that selects the email and ltv columns from a table called user_ltv, which has the following schema: email STRING, age INT, ltv INT. The code also uses the CASE WHEN expression to replace the email values with the string "REDACTED" if the user is not a member of the marketing group. The user who executes the query is not a member of the marketing group, so they will only see the email and ltv columns, and the email column will contain the string "REDACTED" in each row. Verified Reference: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "CASE expression" section.


NEW QUESTION # 79
You are currently asked to work on building a data pipeline, you have noticed that you are currently working with a data source that has a lot of data quality issues and you need to monitor data quality and enforce it as part of the data ingestion process, which of the following tools can be used to address this problem?

  • A. JOBS and TASKS
  • B. STRUCTURED STREAMING with MULTI HOP
  • C. AUTO LOADER
  • D. UNITY Catalog and Data Governance
  • E. DELTA LIVE TABLES

Answer: E

Explanation:
Explanation
The answer is, DELTA LIVE TABLES
Delta live tables expectations can be used to identify and quarantine bad data, all of the data quality metrics are stored in the event logs which can be used to later analyze and monitor.
DELTA LIVE Tables expectations
Below are three types of expectations, make sure to pay attention differences between these three.
Retain invalid records:
Use the expect operator when you want to keep records that violate the expectation. Records that violate the expectation are added to the target dataset along with valid records:
Python
1.@dlt.expect("valid timestamp", "col("timestamp") > '2012-01-01'")
SQL
1.CONSTRAINT valid_timestamp EXPECT (timestamp > '2012-01-01')
Drop invalid records:
Use the expect or drop operator to prevent the processing of invalid records. Records that violate the expectation are dropped from the target dataset:
Python
1.@dlt.expect_or_drop("valid_current_page", "current_page_id IS NOT NULL AND cur-rent_page_title IS NOT NULL") SQL
1.CONSTRAINT valid_current_page EXPECT (current_page_id IS NOT NULL and cur-rent_page_title IS NOT NULL) ON VIOLATION DROP ROW Fail on invalid records:
When invalid records are unacceptable, use the expect or fail operator to halt execution imme-diately when a record fails validation. If the operation is a table update, the system atomically rolls back the transaction:
Python
1.@dlt.expect_or_fail("valid_count", "count > 0")
SQL
1.CONSTRAINT valid_count EXPECT (count > 0) ON VIOLATION FAIL UPDATE


NEW QUESTION # 80
......

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